To annotate the utterances, we will use a politeness analyzer from the recent work of [1]}, where they combine two datasets from [2]} and [3]}.
The politeness regressor model is obtained on these datasets by pre-training a BERT-based model [4]} on Reddit data using masked language modelling, as shown in Figure REF .
The word embeddings are encoded with the transformer encoder, which later is used to classify the politeness values.
As the words are masked, various words can be detected given different levels of the degree of politeness (DoP).
Similarly, given different words, the classifier model can determine various levels of DoP.
This model can also be useful to apply utterance-level context-based politeness learning using hierarchical recurrent neural networks [5]}.
